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Liang T, Sun ZY, Hines MG, Penrose KJ, Hao Y, Chu X, Mellors JW, Dimitrov DS, Xie XQ, Li W, Feng Z. AI-based IsAb2.0 for antibody design. Brief Bioinform 2024; 25:bbae445. [PMID: 39285513 PMCID: PMC11405125 DOI: 10.1093/bib/bbae445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/06/2024] [Accepted: 08/27/2024] [Indexed: 09/22/2024] Open
Abstract
Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.
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Affiliation(s)
- Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Ze-Yu Sun
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Margaret G Hines
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Kerri Jo Penrose
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Yixuan Hao
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Xiaojie Chu
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - John W Mellors
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Dimiter S Dimitrov
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Wei Li
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
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2
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Wang X, Zhang Y, Li Z, Duan Z, Guo M, Wang Z, Zhu F, Xue W. PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering. Nucleic Acids Res 2024; 52:W272-W279. [PMID: 38738624 PMCID: PMC11223824 DOI: 10.1093/nar/gkae384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/23/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024] Open
Abstract
Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.
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Affiliation(s)
- Xiaona Wang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
- Department of Intensive Care Medicine, Army Medical Center of PLA, Chongqing 401331, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zengpeng Li
- State Key Laboratory Breeding Base of Marine Genetic Resources, Third Institute of Oceanography Ministry of Natural Resources, Xiamen 361005, China
| | - Zixin Duan
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Menghan Guo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Zhen Wang
- Department of Intensive Care Medicine, Army Medical Center of PLA, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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3
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Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today 2024; 29:103984. [PMID: 38642702 DOI: 10.1016/j.drudis.2024.103984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China
| | - Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Li Meng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China.
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Li D, Pucci F, Rooman M. Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks. Int J Mol Sci 2024; 25:5434. [PMID: 38791470 PMCID: PMC11121317 DOI: 10.3390/ijms25105434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.
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Affiliation(s)
- Dong Li
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
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5
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Paul R, Kasahara K, Sasaki J, Pérez JF, Matsunaga R, Hashiguchi T, Kuroda D, Tsumoto K. Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction. Front Mol Biosci 2024; 10:1302737. [PMID: 38495738 PMCID: PMC10941800 DOI: 10.3389/fmolb.2023.1302737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/11/2023] [Indexed: 03/19/2024] Open
Abstract
Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.
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Affiliation(s)
- Rimpa Paul
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
| | - Keisuke Kasahara
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Jiei Sasaki
- Institute for Life and Medical Sciences, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Jorge Fernández Pérez
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Ryo Matsunaga
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Takao Hashiguchi
- Institute for Life and Medical Sciences, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
- The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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6
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Luo X, Tong F, Zhao W, Zheng X, Li J, Li J, Zhao D. BERT2DAb: a pre-trained model for antibody representation based on amino acid sequences and 2D-structure. MAbs 2023; 15:2285904. [PMID: 38010801 DOI: 10.1080/19420862.2023.2285904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
Prior research has generated a vast amount of antibody sequences, which has allowed the pre-training of language models on amino acid sequences to improve the efficiency of antibody screening and optimization. However, compared to those for proteins, there are fewer pre-trained language models available for antibody sequences. Additionally, existing pre-trained models solely rely on embedding representations using amino acids or k-mers, which do not explicitly take into account the role of secondary structure features. Here, we present a new pre-trained model called BERT2DAb. This model incorporates secondary structure information based on self-attention to learn representations of antibody sequences. Our model achieves state-of-the-art performance on three downstream tasks, including two antigen-antibody binding classification tasks (precision: 85.15%/94.86%; recall:87.41%/86.15%) and one antigen-antibody complex mutation binding free energy prediction task (Pearson correlation coefficient: 0.77). Moreover, we propose a novel method to analyze the relationship between attention weights and contact states of pairs of subsequences in tertiary structures. This enhances the interpretability of BERT2DAb. Overall, our model demonstrates strong potential for improving antibody screening and design through downstream applications.
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Affiliation(s)
- Xiaowei Luo
- Information Center, Academy of Military Medical Sciences, Beijing, China
| | - Fan Tong
- Information Center, Academy of Military Medical Sciences, Beijing, China
| | - Wenbin Zhao
- Information Center, Academy of Military Medical Sciences, Beijing, China
| | - Xiangwen Zheng
- Information Center, Academy of Military Medical Sciences, Beijing, China
| | - Jiangyu Li
- Information Center, Academy of Military Medical Sciences, Beijing, China
| | - Jing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Dongsheng Zhao
- Information Center, Academy of Military Medical Sciences, Beijing, China
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7
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Yang YX, Wang P, Zhu BT. Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas. J Mol Graph Model 2023; 118:108364. [PMID: 36356467 DOI: 10.1016/j.jmgm.2022.108364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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8
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Gisdon FJ, Kynast JP, Ayyildiz M, Hine AV, Plückthun A, Höcker B. Modular peptide binders - development of a predictive technology as alternative for reagent antibodies. Biol Chem 2022; 403:535-543. [PMID: 35089661 DOI: 10.1515/hsz-2021-0384] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/11/2022] [Indexed: 11/15/2022]
Abstract
Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.
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Affiliation(s)
- Florian J Gisdon
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Josef P Kynast
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Merve Ayyildiz
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
| | - Anna V Hine
- College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
| | - Andreas Plückthun
- Department of Biochemistry, University of Zurich, CH-8057 Zürich, Switzerland
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, D-95447 Bayreuth, Germany
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9
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Wang X, Li F, Qiu W, Xu B, Li Y, Lian X, Yu H, Zhang Z, Wang J, Li Z, Xue W, Zhu F. SYNBIP: synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Res 2021; 50:D560-D570. [PMID: 34664670 PMCID: PMC8728148 DOI: 10.1093/nar/gkab926] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/13/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022] Open
Abstract
The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.
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Affiliation(s)
- Xiaona Wang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Wenqi Qiu
- Department of Surgery, HKU-SZH & Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Binbin Xu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yanlin Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hongyan Yu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Zhao Zhang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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